{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### experiment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dictionaries to organize benchmark data by shot count and qubit configuration\n",
    "benchmark_data = {\n",
    "    '1-qubit':{\n",
    "        '0': pd.read_csv('./benchmark/1-qubit_qubits_44_10000shots_0.csv', dtype=str),\n",
    "        '1': pd.read_csv('./benchmark/1-qubit_qubits_44_10000shots_1.csv', dtype=str),\n",
    "        '2': pd.read_csv('./benchmark/1-qubit_qubits_44_10000shots_2.csv', dtype=str)\n",
    "    },\n",
    "\n",
    "    '2-qubit':{\n",
    "        '0': pd.read_csv('./benchmark/2-qubit_qubits_44-50_10000shots_0.csv', dtype=str),\n",
    "        '1': pd.read_csv('./benchmark/2-qubit_qubits_44-50_10000shots_1.csv', dtype=str),\n",
    "        '2': pd.read_csv('./benchmark/2-qubit_qubits_44-50_10000shots_2.csv', dtype=str)\n",
    "    }\n",
    "}\n",
    "\n",
    "# Create dictionaries to organize test circuit data by shot count and circuit type\n",
    "test_circuit_data = {\n",
    "    # Single qubit Hadamard test circuits\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: pd.read_csv('./test_circuits/1-qubit_hadamard_1_qubits_44_1_1000shots.csv', dtype=str),\n",
    "        5000: pd.read_csv('./test_circuits/1-qubit_hadamard_1_qubits_44_1_5000shots.csv', dtype=str),\n",
    "        10000: pd.read_csv('./test_circuits/1-qubit_hadamard_1_qubits_44_1_10000shots.csv', dtype=str)\n",
    "    },\n",
    "    \n",
    "    # Two-qubit Hadamard test circuits for |00⟩ and |11⟩ states\n",
    "    '2-qubit_hadamard_00and11': {\n",
    "        1000: pd.read_csv('./test_circuits/2-qubit_hadamard_1_qubits_44-50_1_1000shots.csv', dtype=str),\n",
    "        5000: pd.read_csv('./test_circuits/2-qubit_hadamard_1_qubits_44-50_1_5000shots.csv', dtype=str),\n",
    "        10000: pd.read_csv('./test_circuits/2-qubit_hadamard_1_qubits_44-50_1_10000shots.csv', dtype=str)\n",
    "    },\n",
    "    \n",
    "    # Two-qubit Hadamard test circuits for |01⟩ and |10⟩ states\n",
    "    '2-qubit_hadamard_01and10': {\n",
    "        1000: pd.read_csv('./test_circuits/2-qubit_hadamard_2_qubits_44-50_1_1000shots.csv', dtype=str),\n",
    "        5000: pd.read_csv('./test_circuits/2-qubit_hadamard_2_qubits_44-50_1_5000shots.csv', dtype=str),\n",
    "        10000: pd.read_csv('./test_circuits/2-qubit_hadamard_2_qubits_44-50_1_10000shots.csv', dtype=str)\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# Add parent directory to Python path for importing custom modules\n",
    "sys.path.append(os.path.abspath('../..'))\n",
    "\n",
    "from utils.io.pd_process import construct_logical_response_matrix, construct_response_matrix\n",
    "\n",
    "# construct transition matrix\n",
    "Ms = {\n",
    "    '1-qubit':{\n",
    "        '0': construct_response_matrix(groups=[[44]], df=benchmark_data['1-qubit']['0'], normalize=True),\n",
    "        '1': construct_response_matrix(groups=[[44]], df=benchmark_data['1-qubit']['1'], normalize=True),\n",
    "        '2': construct_response_matrix(groups=[[44]], df=benchmark_data['1-qubit']['2'], normalize=True),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        '0': construct_logical_response_matrix(df=benchmark_data['2-qubit']['0'], mapping={'00': '0', '11': '1'}, normalize=True),\n",
    "        '1': construct_logical_response_matrix(df=benchmark_data['2-qubit']['1'], mapping={'00': '0', '11': '1'}, normalize=True),\n",
    "        '2': construct_logical_response_matrix(df=benchmark_data['2-qubit']['2'], mapping={'00': '0', '11': '1'}, normalize=True),\n",
    "    },\n",
    "    \n",
    "    '01and10': {\n",
    "        '0': construct_logical_response_matrix(df=benchmark_data['2-qubit']['0'], mapping={'01': '0', '10': '1'}, normalize=True),\n",
    "        '1': construct_logical_response_matrix(df=benchmark_data['2-qubit']['1'], mapping={'01': '0', '10': '1'}, normalize=True),\n",
    "        '2': construct_logical_response_matrix(df=benchmark_data['2-qubit']['2'], mapping={'01': '0', '10': '1'}, normalize=True),\n",
    "    },\n",
    "\n",
    "    '2-qubit':{\n",
    "        '0': construct_response_matrix(groups=[[44, 50]], df=benchmark_data['2-qubit']['0'], normalize=True),\n",
    "        '1': construct_response_matrix(groups=[[44, 50]], df=benchmark_data['2-qubit']['1'], normalize=True),\n",
    "        '2': construct_response_matrix(groups=[[44, 50]], df=benchmark_data['2-qubit']['2'], normalize=True),\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_Ms = {\n",
    "    '1-qubit': (Ms['1-qubit']['0'] + Ms['1-qubit']['1'] + Ms['1-qubit']['2']) / 3,\n",
    "    '00and11': (Ms['00and11']['0'] + Ms['00and11']['1'] + Ms['00and11']['2']) / 3,\n",
    "    '01and10': (Ms['01and10']['0'] + Ms['01and10']['1'] + Ms['01and10']['2']) / 3,\n",
    "    '2-qubit': (Ms['2-qubit']['0'] + Ms['2-qubit']['1'] + Ms['2-qubit']['2']) / 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check original coding performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.io.pd_process import construct_measured_array\n",
    "\n",
    "test_circuit_data_p = {\n",
    "    # Single qubit Hadamard test circuits\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: construct_measured_array(num_qubits=1, measurement_df=test_circuit_data['1-qubit_hadamard'][1000]),\n",
    "        5000: construct_measured_array(num_qubits=1, measurement_df=test_circuit_data['1-qubit_hadamard'][5000]),\n",
    "        10000: construct_measured_array(num_qubits=1, measurement_df=test_circuit_data['1-qubit_hadamard'][10000])\n",
    "    },\n",
    "    \n",
    "    # Two-qubit Hadamard test circuits for |00⟩ and |11⟩ states\n",
    "    '00and11': {\n",
    "        1000: construct_measured_array(num_qubits=2, measurement_df=test_circuit_data['2-qubit_hadamard_00and11'][1000], mapping={'00': '0', '11': '1'}),\n",
    "        5000: construct_measured_array(num_qubits=2, measurement_df=test_circuit_data['2-qubit_hadamard_00and11'][5000], mapping={'00': '0', '11': '1'}),\n",
    "        10000: construct_measured_array(num_qubits=2, measurement_df=test_circuit_data['2-qubit_hadamard_00and11'][10000], mapping={'00': '0', '11': '1'})\n",
    "    },\n",
    "    \n",
    "    # Two-qubit Hadamard test circuits for |01⟩ and |10⟩ states\n",
    "    '01and10': {\n",
    "        1000: construct_measured_array(num_qubits=2, measurement_df=test_circuit_data['2-qubit_hadamard_01and10'][1000], mapping={'01': '0', '10': '1'}),\n",
    "        5000: construct_measured_array(num_qubits=2, measurement_df=test_circuit_data['2-qubit_hadamard_01and10'][5000], mapping={'01': '0', '10': '1'}),\n",
    "        10000: construct_measured_array(num_qubits=2, measurement_df=test_circuit_data['2-qubit_hadamard_01and10'][10000], mapping={'01': '0', '10': '1'})\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## quantify the distance with the original data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1-qubit_hadamard': {1000: array([0.518, 0.482]),\n",
       "  5000: array([0.5228, 0.4772]),\n",
       "  10000: array([0.5314, 0.4686])},\n",
       " '00and11': {1000: array([0.52380952, 0.47619048]),\n",
       "  5000: array([0.52850507, 0.47149493]),\n",
       "  10000: array([0.5223437, 0.4776563])},\n",
       " '01and10': {1000: array([0.53151261, 0.46848739]),\n",
       "  5000: array([0.53386622, 0.46613378]),\n",
       "  10000: array([0.52820621, 0.47179379])}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_circuit_data_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ideal_noise_test_circuit_data = {\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: avg_Ms['1-qubit']@np.ones(2)/2,\n",
    "        5000: avg_Ms['1-qubit']@np.ones(2)/2,\n",
    "        10000: avg_Ms['1-qubit']@np.ones(2)/2\n",
    "    },\n",
    "    '00and11': {\n",
    "        1000: avg_Ms['00and11']@np.ones(2)/2,\n",
    "        5000: avg_Ms['00and11']@np.ones(2)/2,\n",
    "        10000: avg_Ms['00and11']@np.ones(2)/2\n",
    "    },\n",
    "    '01and10': {\n",
    "        1000: avg_Ms['01and10']@np.ones(2)/2,\n",
    "        5000: avg_Ms['01and10']@np.ones(2)/2,\n",
    "        10000: avg_Ms['01and10']@np.ones(2)/2\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.core.quantify import TVD\n",
    "\n",
    "tvd_origin = {\n",
    "    1000: TVD(test_circuit_data_p['1-qubit_hadamard'][1000], np.ones(2)/2),\n",
    "    5000: TVD(test_circuit_data_p['1-qubit_hadamard'][5000], np.ones(2)/2),\n",
    "    10000: TVD(test_circuit_data_p['1-qubit_hadamard'][10000], np.ones(2)/2)\n",
    "}\n",
    "\n",
    "tvd_00and11 = {\n",
    "    1000: TVD(test_circuit_data_p['00and11'][1000], np.ones(2)/2),\n",
    "    5000: TVD(test_circuit_data_p['00and11'][5000], np.ones(2)/2),\n",
    "    10000: TVD(test_circuit_data_p['00and11'][10000], np.ones(2)/2)\n",
    "}\n",
    "\n",
    "tvd_01and10 = {\n",
    "    1000: TVD(test_circuit_data_p['01and10'][1000], np.ones(2)/2),\n",
    "    5000: TVD(test_circuit_data_p['01and10'][5000], np.ones(2)/2),\n",
    "    10000: TVD(test_circuit_data_p['01and10'][10000], np.ones(2)/2)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## show the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x975 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Variation Distance (TVD) values:\n",
      "1-qubit Hadamard: {'1000': np.float64(0.018000000000000016), '5000': np.float64(0.022800000000000015), '10000': np.float64(0.031399999999999983)}\n",
      "00,11 QED: {'1000': np.float64(0.02380952380952378), '5000': np.float64(0.02850506756756757), '10000': np.float64(0.022343700244135428)}\n",
      "01,10 QED: {'1000': np.float64(0.03151260504201678), '5000': np.float64(0.03386621792175015), '10000': np.float64(0.028206211705049666)}\n",
      "\n",
      "## Circuit Performance Improvements\n",
      "\n",
      "Percentage improvement in TVD compared to 1-qubit Hadamard circuit:\n",
      "\n",
      "### 00/11 Circuit vs 1-qubit Hadamard\n",
      "\n",
      "| Shots | Improvement (%) |\n",
      "|--------|----------------|\n",
      "| 1000 | -32.28% |\n",
      "| 5000 | -25.02% |\n",
      "| 10000 | 28.84% |\n",
      "\n",
      "### 01/10 Circuit vs 1-qubit Hadamard\n",
      "\n",
      "| Shots | Improvement (%) |\n",
      "|--------|----------------|\n",
      "| 1000 | -75.07% |\n",
      "| 5000 | -48.54% |\n",
      "| 10000 | 10.17% |\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Set up the plotting style to match high-quality scientific journals\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'Arial',\n",
    "    'font.size': 10,\n",
    "    'axes.linewidth': 1.0,\n",
    "    'axes.labelsize': 12,\n",
    "    'xtick.major.width': 1.0,\n",
    "    'ytick.major.width': 1.0,\n",
    "    'xtick.labelsize': 10,\n",
    "    'ytick.labelsize': 10,\n",
    "    'legend.fontsize': 10,\n",
    "    'legend.frameon': False,\n",
    "    'figure.dpi': 150,\n",
    "    'savefig.dpi': 300,\n",
    "    'savefig.bbox': 'tight'\n",
    "})\n",
    "\n",
    "# Convert the dictionaries to more convenient format for plotting\n",
    "shots = [1000, 5000, 10000]\n",
    "tvd_values = {\n",
    "    '1-qubit Hadamard': [tvd_origin[1000], tvd_origin[5000], tvd_origin[10000]],\n",
    "    '00,11 QED': [tvd_00and11[1000], tvd_00and11[5000], tvd_00and11[10000]],\n",
    "    '01,10 QED': [tvd_01and10[1000], tvd_01and10[5000], tvd_01and10[10000]]\n",
    "}\n",
    "\n",
    "# Create the figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# Define a color palette that matches scientific publications\n",
    "colors = ['#0072B2', '#D55E00', '#009E73']\n",
    "markers = ['o', 's', '^']\n",
    "\n",
    "# Plot the data with error bars\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    ax.plot(shots, tvd_data, marker=markers[i], markersize=8, \n",
    "            color=colors[i], label=circuit_type, linewidth=2)\n",
    "    \n",
    "    # Add points with a slight offset for better visibility\n",
    "    for j, (shot, tvd) in enumerate(zip(shots, tvd_data)):\n",
    "        ax.scatter(shot, tvd, s=80, facecolors='white', \n",
    "                  edgecolors=colors[i], linewidth=1.5, zorder=3)\n",
    "\n",
    "# Set up the figure for bar plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6.5))\n",
    "\n",
    "# Set width of bars and positions\n",
    "bar_width = 0.25\n",
    "positions = np.arange(len(shots))\n",
    "\n",
    "# Create grouped bar chart\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    offset = (i - 1) * bar_width\n",
    "    bars = ax.bar(positions + offset, tvd_data, bar_width, \n",
    "                 color=colors[i], label=circuit_type, edgecolor='black', linewidth=1)\n",
    "    \n",
    "    # # Add value labels on top of bars\n",
    "    # for j, bar in enumerate(bars):\n",
    "    #     height = bar.get_height()\n",
    "    #     ax.text(bar.get_x() + bar.get_width()/2., height + 0.005,\n",
    "    #             f'{height:.3f}', ha='center', va='bottom', fontsize=9)\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Number of Shots', fontweight='bold')\n",
    "ax.set_ylabel('Total Variation Distance (TVD)', fontweight='bold')\n",
    "ax.set_title('TVD vs. Number of Shots', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Set x-ticks to show shot counts\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([str(shot) for shot in shots])\n",
    "\n",
    "# Add a horizontal line at TVD = 0 for reference\n",
    "ax.axhline(y=0, color='gray', linestyle='--', alpha=0.5, linewidth=1)\n",
    "\n",
    "# Customize the grid (only horizontal grid lines for bar charts)\n",
    "ax.yaxis.grid(True, linestyle='--', alpha=0.7)\n",
    "ax.set_ylim(0, 0.045)\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# Add minor ticks for y-axis\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "\n",
    "# Customize the legend\n",
    "ax.legend(loc='upper right', frameon=True, fancybox=False, edgecolor='black', framealpha=0.8)\n",
    "\n",
    "# Add annotations to explain the meaning of TVD\n",
    "ax.text(0.02, 0.02, 'Lower TVD = Better fidelity to ideal distribution', \n",
    "        transform=ax.transAxes, fontsize=9, style='italic')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print the numerical values for reference\n",
    "print(\"Total Variation Distance (TVD) values:\")\n",
    "for circuit_type, tvd_data in tvd_values.items():\n",
    "    print(f\"{circuit_type}: {dict(zip([str(s) for s in shots], tvd_data))}\")\n",
    "\n",
    "# Calculate improvement percentages\n",
    "improvements = {\n",
    "    '00and11 vs 1-qubit': {},\n",
    "    '01and10 vs 1-qubit': {}\n",
    "}\n",
    "\n",
    "# Calculate improvement for each shot count\n",
    "for i, shots in enumerate([1000, 5000, 10000]):\n",
    "    # 00and11 vs 1-qubit improvement\n",
    "    hadamard_tvd = tvd_values['1-qubit Hadamard'][i]\n",
    "    zerozero_tvd = tvd_values['00,11 QED'][i]\n",
    "    improvement = ((hadamard_tvd - zerozero_tvd) / hadamard_tvd) * 100\n",
    "    improvements['00and11 vs 1-qubit'][shots] = improvement\n",
    "    \n",
    "    # 01and10 vs 1-qubit improvement\n",
    "    zeroone_tvd = tvd_values['01,10 QED'][i]\n",
    "    improvement = ((hadamard_tvd - zeroone_tvd) / hadamard_tvd) * 100\n",
    "    improvements['01and10 vs 1-qubit'][shots] = improvement\n",
    "\n",
    "# Print markdown formatted results\n",
    "print(\"\\n## Circuit Performance Improvements\")\n",
    "print(\"\\nPercentage improvement in TVD compared to 1-qubit Hadamard circuit:\\n\")\n",
    "\n",
    "print(\"### 00/11 Circuit vs 1-qubit Hadamard\")\n",
    "print(\"\\n| Shots | Improvement (%) |\")\n",
    "print(\"|--------|----------------|\")\n",
    "for shots, imp in improvements['00and11 vs 1-qubit'].items():\n",
    "    print(f\"| {shots} | {imp:.2f}% |\")\n",
    "\n",
    "print(\"\\n### 01/10 Circuit vs 1-qubit Hadamard\") \n",
    "print(\"\\n| Shots | Improvement (%) |\")\n",
    "print(\"|--------|----------------|\")\n",
    "for shots, imp in improvements['01and10 vs 1-qubit'].items():\n",
    "    print(f\"| {shots} | {imp:.2f}% |\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# apply mitigation method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.core.algs import matrix_inversion_mitigation, IBU\n",
    "\n",
    "\n",
    "# apply matrix inversion mitigation\n",
    "MI_p = {\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: matrix_inversion_mitigation(test_circuit_data_p['1-qubit_hadamard'][1000], avg_Ms['1-qubit']),\n",
    "        5000: matrix_inversion_mitigation(test_circuit_data_p['1-qubit_hadamard'][5000], avg_Ms['1-qubit']),\n",
    "        10000: matrix_inversion_mitigation(test_circuit_data_p['1-qubit_hadamard'][10000], avg_Ms['1-qubit'])\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        1000: matrix_inversion_mitigation(test_circuit_data_p['00and11'][1000], avg_Ms['00and11']),\n",
    "        5000: matrix_inversion_mitigation(test_circuit_data_p['00and11'][5000], avg_Ms['00and11']),\n",
    "        10000: matrix_inversion_mitigation(test_circuit_data_p['00and11'][10000], avg_Ms['00and11'])\n",
    "    },\n",
    "    \n",
    "    '01and10': {\n",
    "        1000: matrix_inversion_mitigation(test_circuit_data_p['01and10'][1000], avg_Ms['01and10']),\n",
    "        5000: matrix_inversion_mitigation(test_circuit_data_p['01and10'][5000], avg_Ms['01and10']),\n",
    "        10000: matrix_inversion_mitigation(test_circuit_data_p['01and10'][10000], avg_Ms['01and10'])\n",
    "    }\n",
    "}\n",
    "\n",
    "# apply IBU\n",
    "IBU_p = {\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: IBU(test_circuit_data_p['1-qubit_hadamard'][1000], np.ones(2)/2, avg_Ms['1-qubit'], 10),\n",
    "        5000: IBU(test_circuit_data_p['1-qubit_hadamard'][5000], np.ones(2)/2, avg_Ms['1-qubit'], 10),\n",
    "        10000: IBU(test_circuit_data_p['1-qubit_hadamard'][10000], np.ones(2)/2, avg_Ms['1-qubit'], 10),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        1000: IBU(test_circuit_data_p['00and11'][1000], np.ones(2)/2, avg_Ms['00and11'], 10),\n",
    "        5000: IBU(test_circuit_data_p['00and11'][5000], np.ones(2)/2, avg_Ms['00and11'], 10),\n",
    "        10000: IBU(test_circuit_data_p['00and11'][10000], np.ones(2)/2, avg_Ms['00and11'], 10),\n",
    "    },\n",
    "\n",
    "    '01and10': {\n",
    "        1000: IBU(test_circuit_data_p['01and10'][1000], np.ones(2)/2, avg_Ms['01and10'], 10),\n",
    "        5000: IBU(test_circuit_data_p['01and10'][5000], np.ones(2)/2, avg_Ms['01and10'], 10),\n",
    "        10000: IBU(test_circuit_data_p['01and10'][10000], np.ones(2)/2, avg_Ms['01and10'], 10),\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvd_MI = {\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: TVD(MI_p['1-qubit_hadamard'][1000], np.ones(2)/2),\n",
    "        5000: TVD(MI_p['1-qubit_hadamard'][5000], np.ones(2)/2),\n",
    "        10000: TVD(MI_p['1-qubit_hadamard'][10000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        1000: TVD(MI_p['00and11'][1000], np.ones(2)/2),\n",
    "        5000: TVD(MI_p['00and11'][5000], np.ones(2)/2),\n",
    "        10000: TVD(MI_p['00and11'][10000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '01and10': {\n",
    "        1000: TVD(MI_p['01and10'][1000], np.ones(2)/2),\n",
    "        5000: TVD(MI_p['01and10'][5000], np.ones(2)/2),\n",
    "        10000: TVD(MI_p['01and10'][10000], np.ones(2)/2),\n",
    "    }\n",
    "}\n",
    "\n",
    "tvd_IBU = {\n",
    "    '1-qubit_hadamard': {\n",
    "        1000: TVD(IBU_p['1-qubit_hadamard'][1000], np.ones(2)/2),\n",
    "        5000: TVD(IBU_p['1-qubit_hadamard'][5000], np.ones(2)/2),\n",
    "        10000: TVD(IBU_p['1-qubit_hadamard'][10000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '00and11': {\n",
    "        1000: TVD(IBU_p['00and11'][1000], np.ones(2)/2),\n",
    "        5000: TVD(IBU_p['00and11'][5000], np.ones(2)/2),\n",
    "        10000: TVD(IBU_p['00and11'][10000], np.ones(2)/2),\n",
    "    },\n",
    "\n",
    "    '01and10': {\n",
    "        1000: TVD(IBU_p['01and10'][1000], np.ones(2)/2),\n",
    "        5000: TVD(IBU_p['01and10'][5000], np.ones(2)/2),\n",
    "        10000: TVD(IBU_p['01and10'][10000], np.ones(2)/2),\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## show the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x975 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Variation Distance (TVD) values:\n",
      "1-qubit Hadamard: {'1000': np.float64(0.00871068213711107), '5000': np.float64(0.013639319574219072), '10000': np.float64(0.022469794982371172)}\n",
      "00,11 QED: {'1000': np.float64(0.023759905129082964), '5000': np.float64(0.028455938159165722), '10000': np.float64(0.022293928826009335)}\n",
      "01,10 QED: {'1000': np.float64(0.03150096100834271), '5000': np.float64(0.03385497502476917), '10000': np.float64(0.028194004148078794)}\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Set up the plotting style to match high-quality scientific journals\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'Arial',\n",
    "    'font.size': 10,\n",
    "    'axes.linewidth': 1.0,\n",
    "    'axes.labelsize': 12,\n",
    "    'xtick.major.width': 1.0,\n",
    "    'ytick.major.width': 1.0,\n",
    "    'xtick.labelsize': 10,\n",
    "    'ytick.labelsize': 10,\n",
    "    'legend.fontsize': 10,\n",
    "    'legend.frameon': False,\n",
    "    'figure.dpi': 150,\n",
    "    'savefig.dpi': 300,\n",
    "    'savefig.bbox': 'tight'\n",
    "})\n",
    "\n",
    "# Convert the dictionaries to more convenient format for plotting\n",
    "shots = [1000, 5000, 10000]\n",
    "# tvd_values = {\n",
    "#     '1-qubit Hadamard': [tvd_MI['1-qubit_hadamard'][1000], tvd_MI['1-qubit_hadamard'][5000], tvd_MI['1-qubit_hadamard'][10000]],\n",
    "#     '00,11 QED': [tvd_MI['00and11'][1000], tvd_MI['00and11'][5000], tvd_MI['00and11'][10000]],\n",
    "#     '01,10 QED': [tvd_MI['01and10'][1000], tvd_MI['01and10'][5000], tvd_MI['01and10'][10000]]\n",
    "# }\n",
    "\n",
    "tvd_values = {\n",
    "    '1-qubit Hadamard': [tvd_IBU['1-qubit_hadamard'][1000], tvd_IBU['1-qubit_hadamard'][5000], tvd_IBU['1-qubit_hadamard'][10000]],\n",
    "    '00,11 QED': [tvd_IBU['00and11'][1000], tvd_IBU['00and11'][5000], tvd_IBU['00and11'][10000]],\n",
    "    '01,10 QED': [tvd_IBU['01and10'][1000], tvd_IBU['01and10'][5000], tvd_IBU['01and10'][10000]]\n",
    "}\n",
    "\n",
    "# Create the figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# Define a color palette that matches scientific publications\n",
    "colors = ['#0072B2', '#D55E00', '#009E73']\n",
    "markers = ['o', 's', '^']\n",
    "\n",
    "# Plot the data with error bars\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    ax.plot(shots, tvd_data, marker=markers[i], markersize=8, \n",
    "            color=colors[i], label=circuit_type, linewidth=2)\n",
    "    \n",
    "    # Add points with a slight offset for better visibility\n",
    "    for j, (shot, tvd) in enumerate(zip(shots, tvd_data)):\n",
    "        ax.scatter(shot, tvd, s=80, facecolors='white', \n",
    "                  edgecolors=colors[i], linewidth=1.5, zorder=3)\n",
    "\n",
    "# Set up the figure for bar plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6.5))\n",
    "\n",
    "# Set width of bars and positions\n",
    "bar_width = 0.25\n",
    "positions = np.arange(len(shots))\n",
    "\n",
    "# Create grouped bar chart\n",
    "for i, (circuit_type, tvd_data) in enumerate(tvd_values.items()):\n",
    "    offset = (i - 1) * bar_width\n",
    "    bars = ax.bar(positions + offset, tvd_data, bar_width, \n",
    "                 color=colors[i], label=circuit_type, edgecolor='black', linewidth=1)\n",
    "    \n",
    "    # # Add value labels on top of bars\n",
    "    # for j, bar in enumerate(bars):\n",
    "    #     height = bar.get_height()\n",
    "    #     ax.text(bar.get_x() + bar.get_width()/2., height + 0.005,\n",
    "    #             f'{height:.3f}', ha='center', va='bottom', fontsize=9)\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Number of Shots', fontweight='bold')\n",
    "ax.set_ylabel('Total Variation Distance (TVD)', fontweight='bold')\n",
    "ax.set_title('Quantum Circuit Fidelity vs. Number of Shots', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Set x-ticks to show shot counts\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([str(shot) for shot in shots])\n",
    "\n",
    "# Add a horizontal line at TVD = 0 for reference\n",
    "ax.axhline(y=0, color='gray', linestyle='--', alpha=0.5, linewidth=1)\n",
    "\n",
    "# Customize the grid (only horizontal grid lines for bar charts)\n",
    "ax.yaxis.grid(True, linestyle='--', alpha=0.7)\n",
    "ax.set_ylim(0, 0.045)\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# Add minor ticks for y-axis\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "\n",
    "# Customize the legend\n",
    "ax.legend(loc='upper right', frameon=True, fancybox=False, edgecolor='black', framealpha=0.8)\n",
    "\n",
    "# Add annotations to explain the meaning of TVD\n",
    "ax.text(0.02, 0.02, 'Lower TVD = Better fidelity to ideal distribution', \n",
    "        transform=ax.transAxes, fontsize=9, style='italic')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print the numerical values for reference\n",
    "print(\"Total Variation Distance (TVD) values:\")\n",
    "for circuit_type, tvd_data in tvd_values.items():\n",
    "    print(f\"{circuit_type}: {dict(zip([str(s) for s in shots], tvd_data))}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analyse fluctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Noise 0.01, Shots 100: 100%|██████████| 100/100 [00:00<00:00, 33343.70it/s]\n",
      "Noise 0.01, Shots 500: 100%|██████████| 100/100 [00:00<00:00, 32263.88it/s]\n",
      "Noise 0.01, Shots 1000: 100%|██████████| 100/100 [00:00<00:00, 30030.10it/s]\n",
      "Noise 0.01, Shots 5000: 100%|██████████| 100/100 [00:00<00:00, 63167.23it/s]\n",
      "Noise 0.01, Shots 10000: 100%|██████████| 100/100 [00:00<00:00, 33211.69it/s]\n",
      "Noise 0.01, Shots 50000: 100%|██████████| 100/100 [00:00<00:00, 63656.15it/s]\n",
      "Noise 0.05, Shots 100: 100%|██████████| 100/100 [00:00<00:00, 49831.34it/s]\n",
      "Noise 0.05, Shots 500: 100%|██████████| 100/100 [00:00<00:00, 44257.72it/s]\n",
      "Noise 0.05, Shots 1000: 100%|██████████| 100/100 [00:00<00:00, 49648.48it/s]\n",
      "Noise 0.05, Shots 5000: 100%|██████████| 100/100 [00:00<00:00, 33356.96it/s]\n",
      "Noise 0.05, Shots 10000: 100%|██████████| 100/100 [00:00<00:00, 47473.73it/s]\n",
      "Noise 0.05, Shots 50000: 100%|██████████| 100/100 [00:00<00:00, 28661.36it/s]\n",
      "Noise 0.1, Shots 100: 100%|██████████| 100/100 [00:00<00:00, 44682.05it/s]\n",
      "Noise 0.1, Shots 500: 100%|██████████| 100/100 [00:00<00:00, 45114.60it/s]\n",
      "Noise 0.1, Shots 1000: 100%|██████████| 100/100 [00:00<00:00, 49742.69it/s]\n",
      "Noise 0.1, Shots 5000: 100%|██████████| 100/100 [00:00<00:00, 41972.42it/s]\n",
      "Noise 0.1, Shots 10000: 100%|██████████| 100/100 [00:00<00:00, 32271.32it/s]\n",
      "Noise 0.1, Shots 50000: 100%|██████████| 100/100 [00:00<00:00, 48472.25it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2250x1462.5 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Total Variation Distance (TVD) values:\n",
      "\n",
      "Calibration Error Level: 0.01\n",
      "  100 shots: 0.107896 ± 0.056531\n",
      "  500 shots: 0.052660 ± 0.022580\n",
      "  1000 shots: 0.036135 ± 0.014901\n",
      "  5000 shots: 0.022759 ± 0.008000\n",
      "  10000 shots: 0.020962 ± 0.006903\n",
      "  50000 shots: 0.019403 ± 0.002847\n",
      "\n",
      "Calibration Error Level: 0.05\n",
      "  100 shots: 0.178198 ± 0.077733\n",
      "  500 shots: 0.113350 ± 0.034230\n",
      "  1000 shots: 0.108197 ± 0.023229\n",
      "  5000 shots: 0.104473 ± 0.013443\n",
      "  10000 shots: 0.101021 ± 0.009521\n",
      "  50000 shots: 0.101601 ± 0.003965\n",
      "\n",
      "Calibration Error Level: 0.1\n",
      "  100 shots: 0.123923 ± 0.056940\n",
      "  500 shots: 0.059234 ± 0.026889\n",
      "  1000 shots: 0.044183 ± 0.020244\n",
      "  5000 shots: 0.018022 ± 0.008462\n",
      "  10000 shots: 0.013383 ± 0.005295\n",
      "  50000 shots: 0.005630 ± 0.002560\n"
     ]
    }
   ],
   "source": [
    "# Analysis of Measurement Error Propagation in Quantum State Reconstruction\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.linalg import inv\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Set plot style for publication quality\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "sns.set_context(\"paper\", font_scale=1.2)\n",
    "\n",
    "# Define a function to create a transition matrix with some noise\n",
    "def create_transition_matrix(dim, noise_level=0.05):\n",
    "    \"\"\"\n",
    "    Create a transition matrix with controllable noise level.\n",
    "    \n",
    "    Args:\n",
    "        dim: Dimension of the square matrix\n",
    "        noise_level: Standard deviation of the Gaussian noise added to matrix elements\n",
    "    \n",
    "    Returns:\n",
    "        M: Transition matrix (should be close to stochastic)\n",
    "    \"\"\"\n",
    "    # Start with an identity matrix\n",
    "    M = np.eye(dim)\n",
    "    \n",
    "    # Add some off-diagonal elements to make it more realistic\n",
    "    for i in range(dim):\n",
    "        for j in range(dim):\n",
    "            if i != j:\n",
    "                M[i, j] = np.random.uniform(0, 0.3)\n",
    "    \n",
    "    # Normalize rows to make it row-stochastic (each row sums to 1)\n",
    "    for i in range(dim):\n",
    "        M[i, :] = M[i, :] / np.sum(M[i, :])\n",
    "    \n",
    "    # Add noise to represent calibration errors\n",
    "    M += np.random.normal(0, noise_level, size=(dim, dim))\n",
    "    \n",
    "    # Re-normalize to ensure it remains approximately stochastic\n",
    "    for i in range(dim):\n",
    "        M[i, :] = np.abs(M[i, :]) / np.sum(np.abs(M[i, :]))\n",
    "    \n",
    "    return M\n",
    "\n",
    "# Define a function to simulate measurements with statistical fluctuations\n",
    "def simulate_measurement(true_state, M, num_shots):\n",
    "    \"\"\"\n",
    "    Simulate a measurement process with statistical fluctuations.\n",
    "    \n",
    "    Args:\n",
    "        true_state: The true state vector (probabilities should sum to 1)\n",
    "        M: Transition matrix\n",
    "        num_shots: Number of measurement shots\n",
    "    \n",
    "    Returns:\n",
    "        measured_state: The measured state vector with statistical noise\n",
    "    \"\"\"\n",
    "    # Calculate the ideal measured probabilities\n",
    "    ideal_measured = M @ true_state\n",
    "    \n",
    "    # Simulate sampling from this distribution\n",
    "    counts = np.random.multinomial(num_shots, ideal_measured)\n",
    "    \n",
    "    # Convert counts to probabilities\n",
    "    measured_state = counts / num_shots\n",
    "    \n",
    "    return measured_state\n",
    "\n",
    "# Define a function to reconstruct the true state from measurements\n",
    "def reconstruct_state(measured_state, M):\n",
    "    \"\"\"\n",
    "    Reconstruct the true state from measured data using matrix inversion.\n",
    "    \n",
    "    Args:\n",
    "        measured_state: The measured state vector\n",
    "        M: Transition matrix\n",
    "    \n",
    "    Returns:\n",
    "        reconstructed_state: The reconstructed state vector\n",
    "    \"\"\"\n",
    "    try:\n",
    "        M_inv = inv(M)\n",
    "        reconstructed_state = M_inv @ measured_state\n",
    "        \n",
    "        # Handle potential negative probabilities by normalization\n",
    "        reconstructed_state = np.maximum(reconstructed_state, 0)\n",
    "        if np.sum(reconstructed_state) > 0:\n",
    "            reconstructed_state = reconstructed_state / np.sum(reconstructed_state)\n",
    "            \n",
    "        return reconstructed_state\n",
    "    except np.linalg.LinAlgError:\n",
    "        # If matrix is singular, use pseudo-inverse\n",
    "        M_inv = np.linalg.pinv(M)\n",
    "        reconstructed_state = M_inv @ measured_state\n",
    "        \n",
    "        # Handle potential negative probabilities\n",
    "        reconstructed_state = np.maximum(reconstructed_state, 0)\n",
    "        if np.sum(reconstructed_state) > 0:\n",
    "            reconstructed_state = reconstructed_state / np.sum(reconstructed_state)\n",
    "            \n",
    "        return reconstructed_state\n",
    "\n",
    "# Define a function to calculate the total variation distance\n",
    "def total_variation_distance(p, q):\n",
    "    \"\"\"\n",
    "    Calculate the total variation distance between two probability distributions.\n",
    "    \n",
    "    Args:\n",
    "        p, q: Probability distributions\n",
    "    \n",
    "    Returns:\n",
    "        TVD: Total variation distance (between 0 and 1)\n",
    "    \"\"\"\n",
    "    return 0.5 * np.sum(np.abs(p - q))\n",
    "\n",
    "# Simulation parameters\n",
    "dim = 4  # Dimension of the state space (e.g., 4 for 2-qubit system)\n",
    "shots_range = [100, 500, 1000, 5000, 10000, 50000]\n",
    "num_trials = 100  # Number of trials for each shot count\n",
    "noise_levels = [0.01, 0.05, 0.1]  # Different levels of matrix calibration error\n",
    "\n",
    "# Initialize results dictionary\n",
    "results = {noise: {shots: [] for shots in shots_range} for noise in noise_levels}\n",
    "\n",
    "# Run the simulation\n",
    "for noise_level in noise_levels:\n",
    "    # Create a transition matrix with the given noise level\n",
    "    M = create_transition_matrix(dim, noise_level)\n",
    "    \n",
    "    # Create a true state (normalized random probabilities)\n",
    "    true_state = np.random.random(dim)\n",
    "    true_state = true_state / np.sum(true_state)\n",
    "    \n",
    "    for shots in shots_range:\n",
    "        tvd_values = []\n",
    "        \n",
    "        for _ in tqdm(range(num_trials), desc=f\"Noise {noise_level}, Shots {shots}\"):\n",
    "            # Simulate measurement\n",
    "            measured_state = simulate_measurement(true_state, M, shots)\n",
    "            \n",
    "            # Reconstruct the state\n",
    "            reconstructed_state = reconstruct_state(measured_state, M)\n",
    "            \n",
    "            # Calculate TVD between true and reconstructed states\n",
    "            tvd = total_variation_distance(true_state, reconstructed_state)\n",
    "            tvd_values.append(tvd)\n",
    "        \n",
    "        # Store the average TVD for this shot count\n",
    "        results[noise_level][shots] = tvd_values\n",
    "\n",
    "# Calculate statistics\n",
    "mean_tvd = {noise: {shots: np.mean(results[noise][shots]) for shots in shots_range} \n",
    "            for noise in noise_levels}\n",
    "std_tvd = {noise: {shots: np.std(results[noise][shots]) for shots in shots_range} \n",
    "           for noise in noise_levels}\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 9))\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "markers = ['o', 's', '^']\n",
    "\n",
    "for i, noise in enumerate(noise_levels):\n",
    "    means = [mean_tvd[noise][shots] for shots in shots_range]\n",
    "    stds = [std_tvd[noise][shots] for shots in shots_range]\n",
    "    \n",
    "    plt.errorbar(shots_range, means, yerr=stds, fmt=markers[i], \n",
    "                 capsize=5, label=f'Calibration Error: {noise}', \n",
    "                 color=colors[i], markersize=8, linewidth=2)\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Number of Measurement Shots', fontweight='bold')\n",
    "plt.ylabel('Total Variation Distance (TVD)', fontweight='bold')\n",
    "plt.title('Error in State Reconstruction vs. Measurement Statistics', \n",
    "          fontsize=14, fontweight='bold')\n",
    "plt.grid(True, which=\"both\", ls=\"--\", alpha=0.7)\n",
    "plt.legend(loc='upper right', frameon=True, fancybox=False, \n",
    "           edgecolor='black', framealpha=0.8)\n",
    "\n",
    "# Add annotations\n",
    "plt.text(0.02, 0.02, 'Lower TVD = Better reconstruction fidelity', \n",
    "         transform=plt.gca().transAxes, fontsize=10, style='italic')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot the distribution of TVD values for different shot counts at a fixed noise level\n",
    "fixed_noise = 0.05  # Choose a fixed noise level for detailed analysis\n",
    "plt.figure(figsize=(15, 9.75))\n",
    "\n",
    "for i, shots in enumerate(shots_range):\n",
    "    plt.subplot(2, 3, i+1)\n",
    "    sns.histplot(results[fixed_noise][shots], kde=True, \n",
    "                 color=colors[1], alpha=0.7, bins=15)\n",
    "    plt.title(f'{shots} Shots')\n",
    "    plt.xlabel('Total Variation Distance')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.axvline(mean_tvd[fixed_noise][shots], color='red', linestyle='--', \n",
    "                label=f'Mean: {mean_tvd[fixed_noise][shots]:.4f}')\n",
    "    plt.legend()\n",
    "\n",
    "plt.suptitle(f'Distribution of Reconstruction Errors (Calibration Error = {fixed_noise})', \n",
    "             fontsize=16, fontweight='bold')\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.show()\n",
    "\n",
    "# Print numerical results\n",
    "print(\"Mean Total Variation Distance (TVD) values:\")\n",
    "for noise in noise_levels:\n",
    "    print(f\"\\nCalibration Error Level: {noise}\")\n",
    "    for shots in shots_range:\n",
    "        print(f\"  {shots} shots: {mean_tvd[noise][shots]:.6f} ± {std_tvd[noise][shots]:.6f}\")\n"
   ]
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